Dynamic assignment of probability distribution of extrinsic information for turbo decoding over AWGN and Rayleigh fading channels
Bibliographic record
Abstract
This paper proposes a strategy using a generalized Gaussian distribution (GGD) to model the statistical properties of the extrinsic information generated from the component decoders applied to the maximum a posteriori (MAP) algorithm in order to improve the performance of an iterative turbo decoder for finite block lengths. A matching technique based on the measured moments and distance criterion is introduced to dynamically select the appropriate parameter values of the GGD conditional probability density functions of the extrinsic information in each iteration. Simulation is performed to evaluate the performance of the proposed strategy for various block lengths and to examine the statistical behaviour of the intrinsic information in different iterations for both additive white Gaussian noise (AWGN) and the Rayleigh fading channels. The results indicate that the GGD model with dynamic parameter assignment is appropriate to track the varying characteristics of the extrinsic information and helpful for the proposed strategy to offer performance gain in case of medium block lengths.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".